Training data can only be partially observed – they are censored.Īs an example, consider a clinical study, which investigates coronary heart disease and has been carried out over a 1 year period as in the figure below. It differs from traditional regression by the fact that parts of the Survival analysis is a type of regression problem (one wants to predict a continuous value), but with a twist. The name survival analysis originates from clinical research, where predicting the time to death, i.e., survival, is often the main objective. The objective in survival analysis - also referred to as reliability analysis in engineering - is to establish a connection between covariates and the time of an event. Measuring the Performance of Survival Modelsįeature Selection: Which Variable is Most Predictive? The Veterans’ Administration Lung Cancer TrialĬonsidering other variables by stratification It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.

Scikit-survival is a Python module for survival analysis built on top of scikit-learn.

Introduction to Survival Analysis with scikit-survival #

Rules of survival summary